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Recent works have shown that supervised models often exploit data artifactsto achieve good test scores while their performance severely degrades onsamples outside their training distribution. Contrast sets (Gardneret al.,2020) quantify this phenomenon by perturbing test samples in a minimal way suchthat the output label is modified. While most contrast sets were createdmanually, requiring intensive annotation effort, we present a novel methodwhich leverages rich semantic input representation to automatically generatecontrast sets for the visual question answering task. Our method computes theanswer of perturbed questions, thus vastly reducing annotation cost andenabling thorough evaluation of models' performance on various semantic aspects(e.g., spatial or relational reasoning). We demonstrate the effectiveness ofour approach on the GQA dataset and its semantic scene graph imagerepresentation. We find that, despite GQA's compositionality and carefullybalanced label distribution, two high-performing models drop 13-17% in accuracycompared to the original test set. Finally, we show that our automaticperturbation can be applied to the training set to mitigate the degradation inperformance, opening the door to more robust models.

Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA